ChatGPT Doesn't Have A Model Of The World, It Has A Model Of What People Say...,

FT  |   Much has changed since 1986, when the Princeton philosopher Harry Frankfurt published an essay in an obscure journal, Raritan, titled “On Bullshit”. Yet the essay, later republished as a slim bestseller, remains unnervingly relevant. 

Frankfurt’s brilliant insight was that bullshit lies outside the realm of truth and lies. A liar cares about the truth and wishes to obscure it. A bullshitter is indifferent to whether his statements are true: “He just picks them out, or makes them up, to suit his purpose.” Typically for a 20th-century writer, Frankfurt described the bullshitter as “he” rather than “she” or “they”. But now it’s 2023, we may have to refer to the bullshitter as “it” — because a new generation of chatbots are poised to generate bullshit on an undreamt-of scale. 

Consider what happened when David Smerdon, an economist at the University of Queensland, asked the leading chatbot ChatGPT: “What is the most cited economics paper of all time?” ChatGPT said that it was “A Theory of Economic History” by Douglass North and Robert Thomas, published in the Journal of Economic History in 1969 and cited more than 30,000 times since. It added that the article is “considered a classic in the field of economic history”. A good answer, in some ways. In other ways, not a good answer, because the paper does not exist. 

Why did ChatGPT invent this article? Smerdon speculates as follows: the most cited economics papers often have “theory” and “economic” in them; if an article starts “a theory of economic . . . ” then “ . . . history” is a likely continuation. Douglass North, Nobel laureate, is a heavily cited economic historian, and he wrote a book with Robert Thomas. In other words, the citation is magnificently plausible. What ChatGPT deals in is not truth; it is plausibility. What ChatGPT deals in is not truth; it is plausibility 

And how could it be otherwise? ChatGPT doesn’t have a model of the world. Instead, it has a model of the kinds of things that people tend to write. This explains why it sounds so astonishingly believable. It also explains why the chatbot can find it challenging to deliver true answers to some fairly straightforward questions. 

It’s not just ChatGPT. Meta’s shortlived “Galactica” bot was infamous for inventing citations. And it’s not just economics papers. I recently heard from the author Julie Lythcott-Haims, newly elected to Palo Alto’s city council. ChatGPT wrote a story about her victory. “It got so much right and was well written,” she told me. But Lythcott-Haims is black, and ChatGPT gushed about how she was the first black woman to be elected to the city council. Perfectly plausible, completely untrue. 

Gary Marcus, author of Rebooting AI, explained on Ezra Klein’s podcast: “Everything it produces sounds plausible because it’s all derived from things that humans have said. But it doesn’t always know the connections between the things that it’s putting together.” Which prompted Klein’s question, “What does it mean to drive the cost of bullshit to zero”? 

Experts disagree over how serious the confabulation problem is. ChatGPT has made remarkable progress in a very short space of time. Perhaps the next generation, in a year or two, will not suffer from the problem. Marcus thinks otherwise. He argues that the pseudo-facts won’t go away without a fundamental rethink of the way these artificial intelligence systems are built. 

 I’m not qualified to speculate on that question, but one thing is clear enough: there is plenty of demand for bullshit in the world and, if it’s cheap enough, it will be supplied in enormous quantities. Think about how assiduously we now need to defend ourselves against spam, noise and empty virality. And think about how much harder it will be when the online world is filled with interesting text that nobody ever wrote, or fascinating photographs of people and places that do not exist. 

 Consider the famous “fake news” problem, which originally referred to a group of Macedonian teenagers who made up sensational stories for the clicks and thus the advertising revenue. Deception was not their goal; their goal was attention. The Macedonian teens and ChatGPT demonstrate the same point. It’s a lot easier to generate interesting stories if you’re unconstrained by respect for the truth.

I wrote about the bullshit problem in early 2016, before the Brexit referendum and the election of Donald Trump. It was bad then; it’s worse now. After Trump was challenged on Fox News about retweeting some false claim, he replied, “Hey, Bill, Bill, am I gonna check every statistic?” ChatGPT might say the same. Recommended John Gapper ChatGPT is fluent, clever and dangerously creative 

If you care about being right, then yes, you should check. But if you care about being noticed or being admired or being believed, then truth is incidental. ChatGPT says a lot of true things, but it says them only as a byproduct of learning to seem believable

What Should Generative Design Do?

engineering |  Generative design, along with its closely allied technology, topology optimization, is a technology that has overpromised and under-delivered. A parade of parts from generative design providers is dismissed outright as unmanufacturable, impractical—or just goofy looking. Their one saving grace may be that the odd-looking parts save considerable weight compared to parts that engineers have designed but which cannot overcome the fact that they can only be 3D printed, or that their shape is optimized for one load case—and ignores all others. So many stringy “optimized” shapes can be a compressive load that would buckle the part. We could never put that stringy, strange shape in a car, plane or consumer product. We don’t want to be laughed at.

The design software industry, eager to push technology with such potential, acquired at great cost, sees the rejection of generative design as evidence of engineers who are stuck in their ways, content to work with familiar but outdated tools, in the dark and unable to see the light and realize the potential of a game-changing technology. Engineers, on the other hand, say they never asked for generative design—at least not in so many words. 

Like 3D printing, another technology desperate for engineering acceptance, generative design sees its “solutions” as perfect. One such solution was a generatively designed bracket. The odd-looking part was discussed as a modeling experiment by Kevin Quinn, GM’s director of Additive Design and Manufacturing, but with no promise of mass production. It was obviously fragile and relied on 3D printing for its manufacture, making it unmanufacturable at the quantity required. It may have withstood crash test loads, but reverse loading would have splintered it. Yet, the part was to appear in every publication (even ours ) and almost everywhere lauded as a victory for generative design if the saint of lightweighting, a pressing automotive industry priority.

Now more than ever, engineers find themselves leaning into hurricane winds of technology and a software industry that promised us solutions. We are trained to accept technology, to bend it to our will, to improve products we design, but the insistence that software has found a solution to our design problems with generative design puts us in an awkward thanks-but-no-thanks position. We find ourselves in what Gartner refers to as “the trough of disillusionment.”

That is a shame for a technology that, if it were to work and evolve, could be the “aided” in computer- aided design. (For the sake of argument, let’s say that computer-aided design as it exists now is no more than an accurate way to represent a design that an engineer or designer has a fuzzy picture of in their heads).

How much trouble would it be to add some of what we know—our insight—to generative design? After all, that is another technology the software industry is fond of pushing. Watching a topology optimization take shape can be about as painful as watching a roomful of monkeys banging randomly on a keyboard and hoping to write a Shakespeare play. If, by some miracle, they form “What light through yonder window breaks?” our only hope of the right answer would be to type it ourselves. Similarly, an optimization routine starts creating a stringy shape. Bam! Let’s make it a cable and move on. A smooth shape is forming? Jump ahead and make it a flat surface. See a gap forming? Make it a machinable slot. Know a frame will undergo torsion? Stop the madness and use a round tube. (The shapes made with already optimized elements can still be optimized by adjusting angles and lengths.)

The inclusion of AI is what is strangely absent in generative design to this day. We are reminded of a recent conference (pre-pandemic, of course) in which we saw a software vendor go around a generative designed shape, replacing it bit by bit with standard shape elements—a round rod here, a smooth surface there. Really? We should have to do that?

Classical optimization techniques are a separate technology. Like CAD and CAE, they are based on mathematics. Unlike CAD, they have their own language. Optimization borrows language and nomenclature from calculus (optimum, dy/dx = 0, etc.) and adds some of its own. While optimization can be applied to any phenomenon, its application to 3D shapes is most relevant to this discussion. Each iteration of a shape is validated with a numerical technique. For structural shapes, the validation is done with finite element analysis (FEA). For fluid flow optimization, the validation is done with computational fluid dynamics (CFD). Therefore, the application of generative design uses the language of simulation, with terminology like boundary conditions, degrees of freedom, forces and moments. It’s a language foreign to designers and forgotten by the typical product design engineer that runs counter to the democratization of generative design.

The best technology is one that just works, requires little learning, and may not even need an introduction. Think of AI implementations by Google, delivered to our delight, with no fanfare—not even an announcement. Here was Google correcting our spelling, answering our questions, even completing our thoughts and translating languages. Scholars skilled in adapting works from one language to another were startled to find Google equally skilled. Google held no press conference, issued no press release, or even blogged about the wondrous feat of AI. It just worked. And it required no learning.

By contrast, IBM trumpeted its AI technology, Watson, after digesting the sum of human knowledge, easily beating Jeopardy! champion Ken Jennings. But when it came to health care, Watson bombed at the very task it was most heavily promoted for: helping doctors diagnose and cure cancer, according to the Wall Street Journal.

The point is quick success and acceptance will be had with technology that seamlessly integrates into how people already do things and provides delight and a happy surprise. As opposed to retraining, asking users to do things in a whole new way with a new, complicated application that requires them to learn a new language or terminology.

Generative Design: Rules Based Approach To "Creative" Design And Engineering

wikipedia  |  Generative design is an iterative design process that involves a program that will generate a certain number of outputs that meet certain constraints, and a designer that will fine tune the feasible region by selecting specific output or changing input values, ranges and distribution. The designer doesn't need to be a human, it can be a test program in a testing environment or an artificial intelligence, for example a generative adversarial network. The designer learns to refine the program (usually involving algorithms) with each iteration as their design goals become better defined over time.^[1]

The output could be images, sounds, architectural models, animation, and much more. It is therefore a fast method of exploring design possibilities that is used in various design fields such as art, architecture, communication design, and product design.^[2]

The process combined with the power of digital computers that can explore a very large number of possible permutations of a solution enables designers to generate and test brand new options, beyond what a human alone could accomplish, to arrive at a most effective and optimized design. It mimics nature’s evolutionary approach to design through genetic variation and selection.^{[citation needed]}

Generative design has become more important, largely due to new programming environments or scripting capabilities that have made it relatively easy, even for designers with little programming experience, to implement their ideas.^[3] Additionally, this process can create solutions to substantially complex problems that would otherwise be resource-exhaustive with an alternative approach making it a more attractive option for problems with a large or unknown solution set.^[4] It is also facilitated with tools in commercially available CAD packages.^[5] Not only are implementation tools more accessible, but also tools leveraging generative design as a foundation.^[6]

Generative design in architecture

Generative design in architecture is an iterative design process that enables architects to explore a wider solution space with more possibility and creativity.^[7] Architectural design has long been regarded as a wicked problem.^[8] Compared with traditional top-down design approach, generative design can address design problems efficiently, by using a bottom-up paradigm that uses parametric defined rules to generate complex solutions. The solution itself then evolves to a good, if not optimal, solution.^[9] The advantage of using generative design as a design tool is that it does not construct fixed geometries, but take a set of design rules that can generate an infinite set of possible design solutions. The generated design solutions can be more sensitive, responsive, and adaptive to the wicked problem.

Generative design involves rule definition and result analysis which are integrated with the design process.^[10] By defining parameters and rules, the generative approach is able to provide optimized solution for both structural stability and aesthetics. Possible design algorithms include cellular automata, shape grammar, genetic algorithm, space syntax, and most recently, artificial neural network. Due to the high complexity of the solution generated, rule-based computational tools, such as finite element method and topology optimisation, are more preferable to evaluate and optimise the generated solution.^[11] The iterative process provided by computer software enables the trial-and-error approach in design, and involves architects interfering with the optimisation process.

Historical precedent work includes Antoni Gaudí's Sagrada Família, which used rule based geometrical forms for structures,^[12] and Buckminster Fuller's Montreal Biosphere where the rules to generate individual components is designed, rather than the final product.^[13]

More recent generative design cases includes Foster and Partners' Queen Elizabeth II Great Court, where the tessellated glass roof was designed using a geometric schema to define hierarchical relationships, and then the generated solution was optimized based on geometrical and structural requirement.^[14]

Which Industry Sectors Are Working With OpenAI?

You will find more infographics at Statista

statista |  While OpenAI has really risen to fame with the release of ChatGPT in November 2022, the U.S.-based artificial intelligence research and deployment company is about much more than its popular AI-powered chatbot. In fact, OpenAI’s technology is already being used by hundreds of companies around the world.

According to data published by the enterprise software platform Enterprise Apps Today, companies in the technology and education sectors are most likely to take advantage of OpenAI’s solutions, while business services, manufacturing and finance are also high on the list of industries utilizing artificial intelligence in their business processes.

Broadly defined as “the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” artificial intelligence (AI) can now be found in various applications, including for example web search, natural language translation, recommendation systems, voice recognition and autonomous driving. In healthcare, AI can help synthesize large volumes of clinical data to gain a holistic view of the patient, but it’s also used in robotics for surgery, nursing, rehabilitation and orthopedics.

The Tasks AI Should Take Over According To Workers

You will find more infographics at Statista

statista  |  While there are, especially in industries like manufacturing, legitimate fears that robots and artificial intelligence could cost people their jobs, a lot of workers in the United States prefer to look on the positive side, imagining which of the more laborious of their tasks could be taken off their hands by AI.

According to a recent survey by Gartner, 70 percent of U.S. workers would like to utilize AI for their jobs to some degree. As our infographic shows, a fair chunk of respondents also named some tasks which they would be more than happy to give up completely. Data processing is at the top of the list with 36 percent, while an additional 50 percent would at least like AI to help them out in this.

On the other side of the story, as reported by VentureBeat: "Among survey respondents who did not want to use AI at work, privacy and security concerns were cited as the top two reasons for declining AI." To help convince these workers, Gartner recommends "that IT leaders interested in using AI solutions in the workplace gain support for this technology by demonstrating that AI is not meant to replace or take over the workforce. Rather, it can help workers be more effective and work on higher-value tasks."

The Application Of Machine Learning To Evidence Based Medicine

 

What if, bear with me now, what if the phase 3 clinical trials for mRNA therapeutics conducted on billions of unsuspecting, hoodwinked and bamboozled humans, was a new kind of research done to yield a new depth and breadth of clinical data exceptionally useful toward breaking up logjams in clinical terminology as well as experimental sample size? Vaxxed vs. Unvaxxed the subject of long term gubmint surveillance now. To what end?

Nature  | Recently, advances in wearable technologies, data science and machine learning have begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of next-generation ‘deep’ medicine. Despite stunning advances in basic science and technology, clinical translations in major areas of medicine are lagging. While the COVID-19 pandemic exposed inherent systemic limitations of the clinical trial landscape, it also spurred some positive changes, including new trial designs and a shift toward a more patient-centric and intuitive evidence-generation system. In this Perspective, I share my heuristic vision of the future of clinical trials and evidence-based medicine.

Main

The last 30 years have witnessed breathtaking, unparalleled advancements in scientific research—from a better understanding of the pathophysiology of basic disease processes and unraveling the cellular machinery at atomic resolution to developing therapies that alter the course and outcome of diseases in all areas of medicine. Moreover, exponential gains in genomics, immunology, proteomics, metabolomics, gut microbiomes, epigenetics and virology in parallel with big data science, computational biology and artificial intelligence (AI) have propelled these advances. In addition, the dawn of CRISPR–Cas9 technologies has opened a tantalizing array of opportunities in personalized medicine.

Despite these advances, their rapid translation from bench to bedside is lagging in most areas of medicine and clinical research remains outpaced. The drug development and clinical trial landscape continues to be expensive for all stakeholders, with a very high failure rate. In particular, the attrition rate for early-stage developmental therapeutics is quite high, as more than two-thirds of compounds succumb in the ‘valley of death’ between bench and bedside^1,2. To bring a drug successfully through all phases of drug development into the clinic costs more than 1.5–2.5 billion dollars (refs. 3, 4). This, combined with the inherent inefficiencies and deficiencies that plague the healthcare system, is leading to a crisis in clinical research. Therefore, innovative strategies are needed to engage patients and generate the necessary evidence to propel new advances into the clinic, so that they may improve public health. To achieve this, traditional clinical research models should make way for avant-garde ideas and trial designs.

Before the COVID-19 pandemic, the conduct of clinical research had remained almost unchanged for 30 years and some of the trial conduct norms and rules, although archaic, were unquestioned. The pandemic exposed many of the inherent systemic limitations in the conduct of trials⁵ and forced the clinical trial research enterprise to reevaluate all processes—it has therefore disrupted, catalyzed and accelerated innovation in this domain^6,7. The lessons learned should help researchers to design and implement next-generation ‘patient-centric’ clinical trials.

Chronic diseases continue to impact millions of lives and cause major financial strain to society⁸, but research is hampered by the fact that most of the data reside in data silos. The subspecialization of the clinical profession has led to silos within and among specialties; every major disease area seems to work completely independently. However, the best clinical care is provided in a multidisciplinary manner with all relevant information available and accessible. Better clinical research should harness the knowledge gained from each of the specialties to achieve a collaborative model enabling multidisciplinary, high-quality care and continued innovation in medicine. Because many disciplines in medicine view the same diseases differently—for example, infectious disease specialists view COVID-19 as a viral disease while cardiology experts view it as an inflammatory one—cross-discipline approaches will need to respect the approaches of other disciplines. Although a single model may not be appropriate for all diseases, cross-disciplinary collaboration will make the system more efficient to generate the best evidence.

Over the next decade, the application of machine learning, deep neural networks and multimodal biomedical AI is poised to reinvigorate clinical research from all angles, including drug discovery, image interpretation, streamlining electronic health records, improving workflow and, over time, advancing public health (Fig. 1). In addition, innovations in wearables, sensor technology and Internet of Medical Things (IoMT) architectures offer many opportunities (and challenges) to acquire data⁹. In this Perspective, I share my heuristic vision of the future of clinical trials and evidence generation and deliberate on the main areas that need improvement in the domains of clinical trial design, clinical trial conduct and evidence generation.

Fig. 1: Timeline of drug development from the present to the future.

The figure represents the timeline from drug discovery to first-in-human phase 1 trials and ultimately FDA approval. Phase 4 studies occur after FDA approval and can go on for several years. There is an urgent need to reinvigorate clinical trials through drug discovery, interpreting imaging, streamlining electronic health records, and improving workflow, over time advancing public health. AI can aid in many of these aspects in all stages of drug development. DNN, deep neural network; EHR, electronic health records; IoMT, internet of medical things; ML, machine learning.

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Clinical trial design

Trial design is one of the most important steps in clinical research—better protocol designs lead to better clinical trial conduct and faster ‘go/no-go’ decisions. Moreover, losses from poorly designed, failed trials are not only financial but also societal.

Challenges with randomized controlled trials

Randomized controlled trials (RCTs) have been the gold standard for evidence generation across all areas of medicine, as they allow unbiased estimates of treatment effect without confounders. Ideally, every medical treatment or intervention should be tested via a well-powered and well-controlled RCT. However, conducting RCTs is not always feasible owing to challenges in generating evidence in a timely manner, cost, design on narrow populations precluding generalizability, ethical barriers and the time taken to conduct these trials. By the time they are completed and published, RCTs become quickly outdated and, in some cases, irrelevant to the current context. In the field of cardiology alone, 30,000 RCTs have not been completed owing to recruitment challenges¹⁰. Moreover, trials are being designed in isolation and within silos, with many clinical questions remaining unanswered. Thus, traditional trial design paradigms must adapt to contemporary rapid advances in genomics, immunology and precision medicine¹¹.

Russians = Backwardness = Savages = Orcs

Jamestown |   Since 2008, Russia has consistently sought to adopt and introduce command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR) capabilities to the Armed Forces as part of its conventional military modernization plans. At their core, those efforts are rooted in developing a Russian variant of network-centric warfare, reflecting changes in the international strategic environment as well as accompanying transformation in the means and methods of conducting warfare.

After many years of analysis, discussion and planning, the Russian military is now well on the path toward the fuller formation of a network-centric capability that will present challenges for any potential adversary. Thus, Russia’s Armed Forces, together with their numerous technological advances, are confidently entering the high-tech battlespace.

• Military science and military forecasting;
• The character of future conflict;
• Rooting future warfare in the lessons of the past;
• Strategic deterrence and strategic foresight;
• Network-centric warfare;
• War in space;
• Deep defense in information warfare;
• Asymmetric warfare;
• Psychotronic weapons;
• Climate weapons;
• Reflexive control;
• Nanotechnologies.[60]

The concept of network-centric warfare is closely tied to the RMA, with the advances and practical application unfolding through complex processes in the enhancement of US military combat power, particularly in the 1990s. According to Russian military specialists, this meant new means and methods of conducting warfare, integrating “technical reconnaissance, automation and control of fire damage by means of information and telecommunication networks and data transmission to enhance the effectiveness of combat operations through harmonization and coordination of available forces and means based on a common information space.”

The upsurge in interest in network-centric concepts among Russian military scientists since 2008 reflects a clear influence from the senior military and defense leadership. In 2010, Russia’s General Staff Academy published an extensive collection of open-source materials dealing with the concept of network-centric warfare: Setetsentricheskaya voyna: Daydzhest po materialam otkrytykh izdaniy i SMI (Network-Centric Warfare: Digest on Materials of Open Publications and Mass Media).[67] Moreover, the Russian military scientific community continues to maintain considerable focus on network-centric warfare, especially following and analyzing its evolution within the United States military. In 2018, for example S. I. Makarenko and M. S. Ivanov published a 901-page study: Setetsentricheskaya voyna—printsipy, tekhnologii, primery i perspektivy (Network-Centric Warfare—Principals, Technologies, Examples and Perspectives).[68]

It is clear, therefore, that within the existing body of professional Russian science, there is persistent interest in network-centric warfare. But the emerging view of the capability in the Russian context is cautious, and many specialists warn against the state investing too heavily in this area, fearing wastage of resources. As such, these experts tend to counsel against seeing its adoption as a panacea. It is also vital to understand that Russian theorists see network-centric warfare capability as an offensive rather than defensive capability, and they envisage it serving as a tool against other high-technology adversaries.^[69]

In the published writings of Russian military scientists, a deep understanding and body of knowledge exists concerning Western military approaches to network-centric warfare; they tend to analyze the operational experience of such operations and draw conclusions concerning the relative strengths and weaknesses of such approaches. Additionally, Russian specialists have sought to study and draw lessons from examples of Western militaries, such as Sweden’s, that tried and later abandoned efforts to introduce network-centric warfare—in order to avoid these pitfalls in Russia. Russian analyses of US/NATO network-centric capability are also closely linked to how Main Intelligence Directorate (Glavnoye Razvedyvatelnoye Upravleniye—GRU) specialist officers follow, assess and understand the concept and the key trends involved. An outstanding example is Colonel Aleksandr Kondratyev.

 

 

 

Has China Leapfrogged ASML EUV Lithography?

reuters  |  The chief executive of ASML Holding NV, the Dutch semiconductor equipment maker, on Tuesday questioned whether a U.S. push to get the Netherlands to adopt new rules restricting exports to China make sense.

"Maybe they think we should come across the table, but ASML has already sacrificed," CEO Peter Wennink said in an interview with newspaper NRC Handelsblad.

He said that following U.S. pressure, the Dutch government has already restricted ASML from exporting its most advanced lithography machines to China since 2019, something he said has benefited U.S. companies selling alternative technology.

He said that while 15% of ASML's sales are in China, at U.S. chip equipment suppliers "it is 25 or sometimes more than 30%".

A spokesperson for ASML confirmed the remarks in the interview were accurate but declined further comment.

The Biden administration issued new export rules for U.S. companies in October aimed at cutting off China's ability to manufacture advanced semiconductor chips in a bid to slow its military and technological advances.

Washington is urging the Netherlands, Japan and other unspecified countries with companies that make cutting edge manufacturing equipment to adopt similar rules. The Dutch trade minister has confirmed talks are ongoing.

Wennink said it seemed contradictory that U.S. chip manufacturers are able to sell their most advanced chips to Chinese customers, while ASML is only able to sell older chipmaking equipment.

 

 

 

Economy Of Force Is An Aspirational Military Objective

johnhelmer  |  In war, force and money do the talking on the ground. Not talk in the air.

On the electric battlefield in the Ukraine, the targeting of Russian attacks is being calculated to cut the command and control links between the Galician capitals of Lvov and Kiev west of the Dnieper River and the Russian east, according to fresh analyses prepared by a North American military specialist in infrastructure demolition.  

In the first round this month, he says, the missile raids were a “reconnaissance in force. The Russians were experimenting with, and proving, their operational concepts; for instance, how well Iranian drones perform in concert with their other weapons options and tactics. They were  testing NATO counter- measures as well.”

For the time being, this is allowing the wealthy quarters of both cities to enjoy plentiful electricity; even rising house prices according to Kiev realtors in interviews to European media.  They are the sources for western media reporting of how normal and resilient the two cities are.

However, the BBC is now reporting President Vladimir Zelensky as saying  “that 30% of Ukraine’s power stations had been destroyed in the past eight days. Parts of the capital Kyiv have no power and water after new strikes on Tuesday.” The state propaganda organ added: “UK defence intelligence said it was highly likely that Russia had become increasingly willing to strike civilian infrastructure, in addition to military targets, since its setbacks on the battlefield.”

The North American military source has a different assessment. “The power losses in those cities have been targeted to pit those without the money or means for relief against those who have it. The Russian General Staff goal, in my estimation, is not to break the Ukrainian population’s will to fight, or their western backers’ stream of cash and arms. It’s quite the opposite, in fact. The Russians are even allowing the electric trains to keep moving between Lvov and Poland carrying western reporters, rotating NATO staffs, and military resupplies.  It’s to concentrate the new US arms supplies where they can be attacked more cost-effectively in the east; to prevent Zelensky’s men from communicating with their units and with the civilians across the Dnieper, in Kharkov and Odessa; and to allow those who want to leave to head for Poland and Germany. The Russian general who defeated Napoleon once called that his ‘Golden Bridge’ strategy.”  

He is referring to Marshal Mikhail Kutuzov (lead image, left). The deadline  in the Russian calculation is November 15, when President Joseph Biden (centre) will meet President Vladimir Putin (right)  at the G20 summit in Bali, Indonesia, along with a Ukrainian delegation headed by Zelensky. “A quick glance at Ukrainian rail ticket sites shows that the trains are still running between Kiev and Lvov. I don’t believe this is an accident, nor a failure, of the Russian side. With the escalation this week, I believe we are in the attrition phase of the Electric War which coincides with the Ukrainian electricity market data releases, and the approaching Indonesia meeting.”

By sustaining the attacks with low-cost drones, the North American source comments, “it is unlikely that the Ukrainian utility crews, certainly exhausted now and terrified from working around the clock to effect repairs, using what must be dwindling stocks of spares, will be able to keep up.  Where will Ukrainian utilities like DTEK, find in-time spares for 330kV gear that is unique to Russia and the CIS countries? Furthermore, will 1000MVA, 750kV-330kV autotransformers, with all their required metering, control and protection relays, breakers, etc.,  fall out of the sky like the Russian, Iranian, or Turkish drones do? The answers to those questions are nowhere and no.”

“We can anticipate that the strongest attacks, in terms of concentration, accuracy and impact, will occur with lower temperatures.  Now, with winter just weeks, if not days, away, inclement weather including high winds, heavy, wet snow — all famous for knocking down power lines —  will only compound the problems for the Ukrainians.”

“Who can doubt that the Russians will coordinate their strikes with the poor weather, using it as a force multiplier? Just like old Kutuzov did to the French.”

 

U.S. Military Weak - Not For Lack Of $$$ But For Lack Of Ethics, Discipline, and Intelligence

WSJ  |   Americans like to think their military is unbeatable if politicians wouldn’t get in the way. The truth is that U.S. hard power isn’t what it used to be. That’s the message of the Heritage Foundation’s 2023 Index of U.S. Military Strength, which is reported here for the first time and describes a worrisome trend.

Heritage rates the U.S. military as “weak” and “at growing risk of not being able to meet the demands of defending America’s vital national interests.” The weak rating, down from “marginal” a year earlier, is the first in the index’s nine-year history.

The index measures the military’s ability to prevail in two major regional conflicts at once—say, a conflict in the Middle East and a fight on the Korean peninsula. Americans might wish “that the world be a simpler, less threatening place,” as the report notes. But these commitments are part of U.S. national-security strategy.

Heritage says the U.S. military risks being unable to handle even “a single major regional conflict” as it also tries to deter rogues elsewhere. The Trump Administration’s one-time cash infusion has dried up. Pentagon budgets aren’t keeping up with inflation, and the branches are having to make trade-offs about whether to be modern, large, or ready to fight tonight. The decline is especially acute in the Navy and Air Force.

The Navy has been saying for years it needs to grow to at least 350 ships, plus more unmanned platforms. Yet the Navy has shown a “persistent inability to arrest and reverse the continued diminution of its fleet,” the report says. By one analysis it has under-delivered on shipbuilding plans by 10 ships a year on average over the past five years.

From 2005 to 2020, the U.S. fleet grew to 296 warships from 291, while China’s navy grew to 360 from 216. War isn’t won on numbers alone, but China is also narrowing the U.S. technological advantage in every area from aircraft carrier catapults to long-range missiles.

Some will call all this alarmist and ask why the Pentagon can’t do better on an $800 billion budget. The latter is a fair question and the answer requires procurement and other changes. But the U.S. will also have to spend more on defense if it wants to protect its interests and the homeland. The U.S. is spending about 3% of GDP now compared to 5%-6% in the 1980s. The Heritage report is a warning that you can’t deter war, much less win one, on the cheap.

Having Powerful Enemies - But Limited Resources - Focuses The Mind On Weak Spots To Exploit

What used to be called BDA . . . Bomb Damage Assessment, is now satellite reconnaissance imagery review. Based on what is seen, targets are identified as finished or in need of being hit again. This is why last weeks cruise missile attacks have been followed up with subsequent attacks at regular intervals. It takes only minutes to reload launchers. But it takes some hours to collect satellite imagery reflecting recent damage - and have those images reviewed for selecting the next targets.

Low Earth Orbit spacecraft have some memory aboard, but not very much. These are polar orbiting vehicles and the areas over the poles have higher radiation exposure, with memory being notoriously vulnerable to radiation. So, frequent and even perpetual downlinks for Russian assets, is the order of the day. 

Russian spacecraft traversing Ukraine sky north to south or south to north will have Russian receive stations line-of-sight for downlinks of imagery. Ground based jammers aren't heard by dishes pointed upwards from within Russia.

 It is clear that in-orbit assets have determined Russian tactics and strategy. Why Sergei Surovikin, commander of Russian Aerospace forces is now supreme commander of the mobilization to bring about Ukrainian capitulation.

Normal drones have a controller, since they are either surveillance drones or attack drones hunting particular targets.The so-called kamikaze drones do not have a controller and are subsequently immune to jamming. They are instead like low and slow miniature ballistic missiles. Flight path fixed at time of launch so as to hit a particular static target. 

They evade detection till they can be seen near the target because they are small, slow, and very low to the ground. They emit very little infrared so they can’t be detected that way. They don’t talk to the mother ship so they can’t be seen sending signals nor can they be signal jammed. They thus also take way less in the way of chips (simpler and fewer) and so can be made cheaply and quickly in large quantities.

 Kamikaze drones are far cheaper than but just as effective as high-cost precision missiles Best bit is that they follow one of the principles of war – economy of force – and they certainly get a lot of bang for the buck.

The kamikaze drone will bring old fashioned antiaircraft guns back. The ones Russia is using don’t produce enough heat for a MANPAD to lock to, small arms aren’t going to bring them down in most cases, and it sounds like they don’t show up very well on modern missile anti-air systems which is combined with the ridiculous cost of bringing down a $20K drone with a $100K+ missile.

The Ukrainian tactic of putting serious air defense systems inside populated areas is almost as kamikaze as the drones themselves. Having them on the White House or similar makes some sense, having them heavily used inside a city does not.

At the moment, the Ukrainian police, soldiers, militia, etc. are trying to shoot those drones down with rifles, pistols or anything else that shoots a bullet and the streets of Kiev are sounding like a firing range. Best to be inside or you might get hit by a falling bullet-the more real danger is a populated area getting hit by an exploding shot down drone rather than the drone hitting its energy infrastructure target. Except it’s not just bullets flying willy nilly. 

Ukraine has S300 and Buk missiles curving down trying to hit the drones and plowing into apartment blocks. The S300 packs 150kg of explosive to the Geran-2’s 50kg.

And on top of that, keen troopers with western-supplied ATGMs are trying to hit drones in the air. Often with unguided ATGMs. Even  with guided ones they’ve got a snowballs chance. All of those come down too.

Russia has begun flooding* Donbass with old, reliable S-60 anti-aircraft guns. They shoot 57mm shells with proximity fuses, and are mgreat against small drones – as per experience in Syria. They can also penetrate 90mm of steel, so work well also against an enemy largely down to APCs and civilian vehicles for mobility.

And being from the old Soviet stock, they can be easily integrated with the existing air-defense systems, like battalion/divisional radars for targeting information or even automatic targeting.

Designed in the late 40s, considered obsolete in the mid-60s, reinstated after Vietnamese experience in early 70s, finally removed from service to storage in 1990s only find a niche for use again today.

Anyway, once you do see the drone, the latest wisdom is that 57 mm ammo has longer reach (6000 m vs 4000 m), doesn’t rely on hitting the target directly (proximity fuse) and packs way more punch (3-4 times heavier shell) than a regular 30 mm (like Pantsir, Tunguska or BMP-2/3).

Which all apparently translate to a higher kill probability against drone type targets. Come to think of it, S-60 was designed 80 years ago to protect the troops against relatively low and slow flying, propeller driven aerial vehicles.

 

At $100K/Shot Iron Dome Cheaper Than Patriot - But Not Effective Against Drones

wikipedia |  Although Iron Dome has proven its effectiveness against rocket attacks, Defense Ministry officials are concerned it will not be able to handle more massive arsenals possessed by Hezbollah in Lebanon should a conflict arise. Although in Operation Protective Edge it had a 90 percent hit rate against only rockets determined to be headed for populated areas, 735 intercepts were made at a cost of $70,000–100,000 per interceptor; with an estimated 100,000 rockets possessed by Hezbollah, Iron Dome systems could be fiscally and physically overwhelmed by dozens of incoming salvos. In 2014 Directed-energy weapons were being investigated as a complement to Iron Dome, with lower system cost and lower cost per shot. Solid-state lasers worldwide have power levels ranging from 10–40 kW; to destroy a rocket safely from 15–20 km (9.3–12.4 mi) away, several low-power beams could coordinate and converge on one spot to burn through its outer shell and destroy it. Because laser beams become distorted and ineffective in foggy or heavy cloud conditions, any laser weapon would need to be complemented by Iron Dome.^[67]

In 1996, the Israelis developed the Nautilus prototype and later deployed it in Kiryat Shmona, Israel's northernmost city along the Lebanese border. It used a collection of components from other systems and succeeded in keeping a beam on the same point for two continuous seconds using an early prototype of the Green Pine radar. Nautilus succeeded in its goal to prove the concept was feasible, but it was never deployed operationally, as the government believed that sending in ground troops to stop rocket fire at source was more cost-effective.^[67]

At the 2014 Singapore Air Show, Rafael unveiled its Iron Beam laser air-defense system. Iron Beam is a directed-energy weapon made to complement the Iron Dome system by using a high-energy laser to destroy rockets, mortar bombs, and other airborne threats.^[68] Development of the system began some time after the joint United States and Israel Nautilus laser development program ended.^[3]

In December 2014, former Israeli Air Force chief and head of Boeing Israel David Ivry showed interest in the American Laser Weapon System (LaWS). Earlier that month, the U.S. Navy had revealed that the LaWS had been mounted on the USS Ponce and locked onto and destroyed designated targets with near-instantaneous lethality, with each laser shot costing less than $1.^[67]

In February 2022, Israeli Prime Minister Naftali Bennett announced that a ground-based laser system would begin deployment within a year, first as a trial and then operationally. The system will first be deployed to the south of the country to areas most under threat from rockets fired from the Gaza Strip; the ultimate goal is for Israel to be surrounded by a "laser wall" to protect from rockets, missiles, and UAVs.^[69] While lasers are cheaper to fire per shot, they can be impacted by weather, have a slow rate of fire, and have less range. Therefore they will be used in conjunction with Iron Dome in situations where they can reduce overall interception costs.^[70] A procurement contract for the Iron Beam system was signed the next month, however the schedule for fielding was revealed to be delayed for several years.^[71]

At $2 Million/Shot Patriot Was Only Designed To Shoot Down Enemy AIRCRAFT

wikipedia |  The MIM-104 Patriot is a surface-to-air missile (SAM) system, the primary of its kind used by the United States Army and several allied states. It is manufactured by the U.S. defense contractor Raytheon and derives its name from the radar component of the weapon system. The AN/MPQ-53 at the heart of the system is known as the "Phased Array Tracking Radar to Intercept on Target" which is a backronym for PATRIOT. The Patriot system replaced the Nike Hercules system as the U.S. Army's primary High to Medium Air Defense (HIMAD) system and replaced the MIM-23 Hawk system as the U.S. Army's medium tactical air defense system. In addition to these roles, Patriot has been given the function of the U.S. Army's anti-ballistic missile (ABM) system, which is now Patriot's primary mission. The system is expected to stay fielded until at least 2040.^[5]

Patriot uses an advanced aerial interceptor missile and high-performance radar systems. Patriot was developed at Redstone Arsenal in Huntsville, Alabama, which had previously developed the Safeguard ABM system and its component Spartan and hypersonic speed Sprint missiles. The symbol for Patriot is a drawing of a Revolutionary War–era Minuteman.

Patriot systems have been sold to the armed forces of the Netherlands, Poland, Germany, Japan, Israel, Saudi Arabia, Kuwait, Taiwan, Greece, Spain, the United Arab Emirates, Qatar, Romania and Sweden. South Korea purchased several second-hand Patriot systems from Germany after North Korea test-launched ballistic missiles to the Sea of Japan and proceeded with underground nuclear testing in 2006.^[6] Jordan also purchased several second-hand Patriot systems from Germany. Poland hosts training rotations of a battery of U.S. Patriot launchers. This started in the town of Morąg in May 2010, but was later moved further from the Russian border to Toruń and Ustka due to Russian objections.^[7] On December 4, 2012, NATO authorized the deployment of Patriot missile launchers in Turkey to protect the country from missiles fired in the civil war in neighboring Syria.^[8] Patriot was one of the first tactical systems in the U.S. Department of Defense (DoD) to employ lethal autonomy in combat.^[9]

The Patriot system gained prestige during the Persian Gulf War of 1991 with the claimed engagement of over 40 Iraqi Scud missiles. The system was successfully used against Iraqi missiles in 2003 Iraq War, and has also been used by Saudi and Emirati forces in the Yemen conflict against Houthi missile attacks. The Patriot system achieved its first undisputed shootdowns of enemy aircraft in the service of the Israeli Air Defense Command. Israeli MIM-104D batteries shot down two Hamas UAVs during Operation Protective Edge on August 31, 2014, and later, on September 23, 2014, an Israeli Patriot battery shot down a Syrian Air Force Sukhoi Su-24 which had penetrated the airspace of the Golan Heights, achieving the system's first shootdown of a manned enemy aircraft.^[10]

Russian Combat Lasers End "Full Spectrum Dominance" Till Further Notice...,

stalkerzone |  On May 18, Deputy Prime Minister of the government and curator of the Russian defence industry, Yury Borisov, said that the “Peresvet” combat laser system has already been serially supplied to the Russian troops. According to the Deputy Prime Minister, the laser can disable satellites in orbits up to 1,500 kilometres high. Such technologies were previously considered impossible. A significant number of experiments in this area were conducted by two superpowers of the world.

Several similar programs have been active in the US for some time. One of the most promising was considered to be the development work on the topic of the YAL-1 “flying laser” based on the Boeing 747 aircraft. As a result, $12 billion was spent on a high-energy weapons program to intercept ballistic missile warheads, but the work was completed to no avail.

In the USSR, they went the other way. Few people know, but it was the mock-up of the “Skif-DM” combat laser complex, or “Index 17F19DM”, better known as “Polyus”, that was the first “passenger” of the superheavy “Energiya” rocket back in 1987. As with modern anti-satellite weapons, its principle was based on the defeat of the optical elements of enemy satellites – visors and lenses. The second, cheaper and simpler project in this direction is the A-60 chemical laser based on the Il-76 transport aircraft.

“Peresvet” laser and secret “Zadira”

Work on the “Peresvet” combat laser complex was first announced by Russian President Vladimir Putin. The exact composition of the equipment inside the complex is not disclosed, however, it is known that “Peresvet” is a high-energy laser in a mobile version: a generator and a combat readiness maintenance system, a radiator and a surveillance system are located in several sections. According to some reports, the complex is effective against all surveillance means, including RQ-4 Global Hawk high-altitude unmanned vehicles, as well as most spacecraft of the American IMINT species reconnaissance system. According to some reports, commercial structures are also periodically connected to it. The most famous example is MAXAR, which provides high-resolution intelligence to the US military. The other two participants in this program are slightly less well known, but Planet Labs and SkyBox have the most high-tech devices.

Back in 2014, with the help of a complex optical circuit, ultra-sensitive matrices and software processing, Skybox was able to achieve outstanding performance. From a height of 600 km, their devices can film the surface of the planet with a detail of about 1 meter, while it’s not only about photos, but also about video. The project turned out to be so impressive that the entire company was bought out by the IT giant Google, and the satellites formed the basis of the Terra Bella surveillance system. Another company, Planet Labs, received the first “spy grant” back in 2016, and since then it has been commercial structures that have been spying on the most important objects of a likely enemy, including in Russia.

The capabilities of the “Peresvet” laser are designed just for such means of reconnaissance. The principle of operation of the weapon is not disclosed, however, it is known that the previous complexes, designed in the USSR and Russia, could operate in several modes. The two most important ones are the “local impact” mode on a specific vehicle flying over a restricted zone, as well as the “continuous defeat” mode, when over a certain zone (a square of several hundred kilometres) a so-called laser curtain is being put up.

here is no detailed data on the combat deployment of the “Peresvet” complexes during the special military operation in Ukraine, however, in early March, Wired reported that American satellites were “experiencing difficulties” in working when flying over the border areas of Russia and Ukraine. According to Wired, the intelligence department of the US Department of Defence could not get any pictures of the desired area at all before the start of hostilities, and the advanced units of Russian troops on the territory of Ukraine were noticed from space only a few hours after the start of “Operation Z”. Neither civilian analysts nor representatives of the Pentagon specify why this happened.

And on February 28, 4 days after the start of the special military operation in Ukraine, Google satellites “fell off”. The company even had to issue a statement informing users about the “temporary shutdown” of updates for images in areas of concentration and movement of Russian troops. In total, according to Professor Todd Humphreys of the University of Texas, at least 50 different synthetic-aperture radar (SAR) satellites were deployed over Ukraine after the start of its military operation by the Russian Army.

It is curious that the combat protection of such objects as the “Peresvet” laser is carried out not only by electronic warfare troops and air defence units, but also by fully-fledged “Zadira” combat lasers. This complex was developed at the Russian Federal Nuclear Center (Russian Federal Nuclear Center — All-Russian Research Institute of Experimental Physics) in Sarov. The power of the weapon is not disclosed, however, according to some reports, a few seconds of radiation is enough to “cut” a small reconnaissance drone into two parts.

Starlink to Ukraine

According to some reports, the American and Ukrainian military had several simultaneous serious problems.

Firstly, a significant part of the equipment of the US surveillance satellites “failed” in the first few days of the special operation in Ukraine. The reasons why this happened are not disclosed in the United States, just as in Russia they do not make statements on the combat use of “Peresvet” lasers capable of burning out the powerful optics of satellites in orbits up to 1,500 kilometres high.

Secondly, the transmission of data from Maxar Technologies’ WorldView-2 satellites to Ukrainian spacecraft was difficult, since the latter, as it turned out, were not designed for such a volume of information. Data overload has led to the fact that a significant part of telecommunications satellites simply failed. It is not possible to restore their functionality at the moment, so the spacecraft are just hanging out in orbit like garbage.

It is for this reason that Elon Musk was urgently brought into the arena. Starlink communications satellites, previously tested at military exercises of the US Army and the Strategic Command, turned out to be the most convenient channel for data transmission. If it were not for the data transmission network that SpaceX deployed in orbit, the UAF would have lost all intelligence — both its own and those transmitted to them by American intelligence.